get_attribution_results

Does the description disclose side effects, auth requirements, rate limits, or destructive behavior?

With no annotations, the description carries full burden. It discloses that results come from the most recent Analyst agent run and are distinct from platform-reported attribution, but does not mention prerequisites (e.g., agent run must exist), data freshness, or potential errors. Adequate but not comprehensive.

Agents need to know what a tool does to the world before calling it. Descriptions should go beyond structured annotations to explain consequences.

Is the description appropriately sized, front-loaded, and free of redundancy?

Two sentences: first states core function and results, second adds context and parameter usage. No redundant words, front-loaded with purpose.

Shorter descriptions cost fewer tokens and are easier for agents to parse. Every sentence should earn its place.

Given the tool's complexity, does the description cover enough for an agent to succeed on first attempt?

Given no output schema, description adequately details returned fields (attributed conversions, value, spend, CPA, ROAS, credit share). Mentions distinction from platform-reported attribution. Lacks mention of error cases if no run exists, but otherwise complete for a simple tool.

Complex tools with many parameters or behaviors need more documentation. Simple tools need less. This dimension scales expectations accordingly.

Does the description clarify parameter syntax, constraints, interactions, or defaults beyond what the schema provides?

Schema coverage is 0%, so description must compensate. It explains the parameter 'conversion_type' as a filter for B2B pipeline stages or B2C events, providing examples (e.g., 'opportunity_created'). This adds significant meaning beyond the schema's empty string type.

Input schemas describe structure but not intent. Descriptions should explain non-obvious parameter relationships and valid value ranges.

Does the description clearly state what the tool does and how it differs from similar tools?

Description clearly states the verb 'Get', the resource 'multi-touch attribution results from the most recent Analyst agent run', and details the returned metrics (weighted credit by channel/platform). This distinguishes it from siblings like get_attribution_run_history or compare_attribution_models.

Agents choose between tools based on descriptions. A clear purpose with a specific verb and resource helps agents select the right tool.

Does the description explain when to use this tool, when not to, or what alternatives exist?

The description implies usage for retrieving the latest MTA results and mentions filtering by conversion_type, but does not explicitly contrast with sibling tools like get_attribution_run_history for historical data or compare_attribution_models for model comparison. No guidance on when not to use.

Agents often have multiple tools that could apply. Explicit usage guidance like "use X instead of Y when Z" prevents misuse.